How Financial Services Firms Can Capitalize on Their Data Riches

How data can be used to refine targeted campaigns, acquire new customers, and launch new services.

By Fred Marthoz, VP at Lotame

Between the rise of online shopping, the ease of contactless, and the convenience of electronic wallets, the course has been set for a future of digital finance, where cash and bank branches no longer exist, replaced by taps and online services. In 2021, non-cash payments in the euro area increased by 12.5%, with 64 million open banking customers expected in Europe by 2024.

Let’s take a look at the challenges and opportunities of data driven financial services and how data can be used to refine targeted campaigns, acquire new customers, and launch new services.

How financial technology shook up the finance sector’s attitude to data

Traditional banks, despite often being well-developed in other areas of technology, were not the first to utilise customer data to layer new convenience features on top of core banking services. New, digital-only banks such as Monzo and Revolut disrupted the market with up-to-date technology and a data-driven approach to customer services, making their operations far less complex than established banks that had to support what could be decades-old legacy infrastructure.

Instead, these challenger banks, as they came to be known, focused on convenience. Their entirely app-based interfaces used the customer’s data to show them interesting financial insights laid out in easy-to-read ways, while sending and receiving money was simplified to a messaging interface familiar to any social media user.

With the move to digital finance comes a deluge of data, as traditional banks, their challenger counterparts, and various other fintech players have access to trillions of data points on their customers. The companies that best leverage this data will differentiate themselves with customer-focused services, personalised promotions, and effective marketing.

It wasn’t only challenger banks that were satisfying the growing demand for data-driven services amongst digital audiences. Tech giants such as Amazon, Google, and Apple turned user accounts — and eventually their devices — into portable digital wallets that today can be packed with credit cards from all three. To ensure users leave their walled gardens as little as possible, big tech added fintech to their arsenal.

Traditional banks accelerated their digital transition during the pandemic which necessitated virtual services, but have come out the other side into a far more competitive financial sector than when they entered. Now, we’ve reached the point where what were once innovations such as social messaging payment apps, real-time payments, customer service chatbots, and public cloud banking are now standard features amongst new and long-standing finance players.

Now that digital finance’s core features are on the way to Gartner’s plateau of productivity, delivering better quality services and communications than fast-multiplying rivals is going to be crucial to stand out going forward, and capitalising on data will be key.

How to maximise customer data through enrichment and expansion

One of the side effects of fintech disruption has been the unmooring of customer loyalties. Take bank accounts as an example: people used to stick to one, maybe two services for years due to the lengthy process of switching or opening accounts. Now it’s so easy to access the range of app-based accounts that customers are more likely to shop around, try out competitors, and have multiple accounts going at any one time to access the benefits of each. With so many new and agile players in the market, financial service companies must prioritise acquisition to keep their client numbers healthy.

Banks and fintech firms are in a strong position when it comes to data availability. Not only do they hold user authentication details, such as email addresses and phone numbers, but also information about how logged-in users interact with apps and platforms. Layer purchasing activity on top of this and you have one of the richest data sets out there. This is great for retaining and upselling to existing customers, but how can this help in the search for new ones while maintaining commitments to user privacy?

The answer is lookalike modelling, which identifies prospective customers by using existing user data to find similar profiles  on the web, social networks, CTV platforms, or anywhere else in the connected world. For example, if a mortgage product performs especially well with the “40+ urban professional with children” persona, lookalike modelling allows marketers to target prospects who match this description — often targeting their competitor’s customers.

A targeted marketing strategy is especially valuable for multi-service companies that have to compete on multiple fronts. Take Revolut, for instance, which aims to be an “everything” app for money, having added an investment platform, insurance, and hotel booking on top of their core account services. Not every prospect will be interested in every service, so knowing who to target and how is key to promoting each one effectively, particularly in such an active and competitive sector where customers can hop between providers with minimal friction.

Sorting customer data into profiles also provides the opportunity for enrichment with second- and third-party data sources to gain a deeper understanding of existing clientele beyond their spending habits. Cluster analysis can separate these profiles into groups and subgroups with shared attributes, such as parents, car owners, frequent flyers, and so on.

Finally, financial services must put interoperability at the heart of their data strategy. Ensuring compatibility with the variety of adtech and martech tools that make up the modern data ecosystem allows audiences to be discovered and activated wherever needed. This can drive efficiency in complex omnichannel campaigns, which may mean expanding marketing operations into growing mediums such as CTV.

However, data cannot be moved around recklessly: financial services hold some of the most sensitive and valuable PII out there and any leaks or privacy breaches would be disastrous, making interoperability far more challenging than in other industries. Whatever tools might be used to establish external connections must come with robust and proven solutions for encryption, anonymisation, and consent tracking.

Across all sectors, data has become a critical asset for businesses in remaining agile, relevant, and stable in uncertain times. In the fintech world, the added pressure of mounting competition and ever-evolving customer needs makes accurate insight especially important. To capitalise on their data riches, organisations must reconfigure and unify their data whilst persistently tapping incoming insights to ensure they can provide the services that satisfy their existing customers, attract new ones and enable them to outpace their rivals.